發(fā)布時間：2018-06-28 07:04

  本文選題：腦MRI + 多模態(tài)��； 參考：《杭州電子科技大學(xué)》2014年碩士論文

【摘要】：腦白質(zhì)疏松癥是一種在T2FLAIR的MRI等醫(yī)學(xué)影像中雙側(cè)腦室前后角周圍白質(zhì)、半卵圓中心、放射冠等區(qū)域所表現(xiàn)出的大小不等的斑點狀或融合成片狀的白質(zhì)高信號區(qū)，是中老年的常見病之一。它的病理學(xué)成因到現(xiàn)在為止還并不是很清楚，可能與中風(fēng)、帕金森病、輕度認(rèn)知障礙、阿爾茨海默氏癥、甚至情緒紊亂等有聯(lián)系。同時，在與患者T2FLAIR影像中的白質(zhì)疏松區(qū)域?qū)?yīng)的ESWAN影像的相應(yīng)區(qū)域中可以觀察到腦內(nèi)深部髓質(zhì)靜脈的顯現(xiàn)，它們之間的關(guān)系近年來開始被醫(yī)學(xué)界探索，研究者正試圖通過它們在影像學(xué)上的關(guān)系和表現(xiàn)進(jìn)一步解釋其關(guān)聯(lián)性。臨床醫(yī)生往往通過主觀經(jīng)驗或者簡單的體視學(xué)網(wǎng)格測量對腦白質(zhì)疏松癥程度進(jìn)行評級；而腦內(nèi)深部髓質(zhì)靜脈即使在最明顯的ESWAN影像中仍是管徑細(xì)小、成像模糊、分辨困難。這些主觀評價無法提供一個準(zhǔn)確的論據(jù)，因此需要一種可靠的計算機輔助方法幫助醫(yī)生準(zhǔn)確提取腦白質(zhì)疏松癥病變區(qū)域和腦內(nèi)深部髓質(zhì)靜脈。 針對這樣的需求，，本文根據(jù)兩種病變區(qū)域在多模態(tài)影像下的成像特點，研究了對T2FLAIR影像中腦白質(zhì)疏松癥和ESWAN影像中腦內(nèi)深部髓質(zhì)靜脈主干兩者進(jìn)行自動提取的計算機輔助診斷方法，主要分為三大步：先對T2FLAIR影像進(jìn)行頭骨剝離預(yù)處理；然后在T2FLAIR影像中提取腦白質(zhì)疏松區(qū)域；最后在ESWAN影像的相應(yīng)區(qū)域（與T2FLAIR影像中的腦白質(zhì)疏松區(qū)域?qū)?yīng)）提取腦內(nèi)深部髓質(zhì)靜脈主干。頭骨剝離預(yù)處理先后利用了大津閾值法和形態(tài)學(xué)方法，可以對干擾腦白質(zhì)疏松分割的非大腦組織高信號進(jìn)行有效剝離，實驗中本方法與通用的BSE頭骨剝離方法一起與手動精細(xì)分割金標(biāo)準(zhǔn)相比，在Jaccard相似度和Dice相似度兩個測度上得出本方法上更為準(zhǔn)確。腦白質(zhì)疏松區(qū)域分割則首先通過大津閾值法確定腦組織中腦白質(zhì)疏松區(qū)域的初始輪廓，然后用一種改進(jìn)的C-V模型方法對初始輪廓進(jìn)行迭代演化，對比實驗證明本方法比Li的免初始化的水平集方法在分割效果上表現(xiàn)略好，在分割時間上表現(xiàn)更優(yōu)。最后，利用了Hessian矩陣的特征值與腦內(nèi)深部髓質(zhì)靜脈灰度變化方向之間的關(guān)系設(shè)計了一種篩選器，用于腦內(nèi)深部髓質(zhì)靜脈主干的提取，該篩選器能夠初步篩選出有效主干，再結(jié)合形態(tài)學(xué)后處理連接斷開主干，剔除偽主干，結(jié)果與手動精細(xì)分割的金標(biāo)準(zhǔn)比較接近。
[Abstract]:Leukoaraiosis is an area of white matter in the white matter around the anterior and posterior angles of the ventricle, the center of the semiovale, the corona, and other regions in the MRI images of T2FLAIR. It is one of the common diseases in the middle and old age. Its pathogenetic causes are still unclear and may be associated with stroke, Parkinson's disease, mild cognitive impairment, Alzheimer's disease, and even emotional disorders. At the same time, the deep medullary veins in the brain can be observed in the corresponding regions of ESWAN images corresponding to the areas of leukoaraiosis in T2FLAIR images of patients. The relationship between them has been explored by the medical community in recent years. The researchers are trying to further explain their relevance through their imaging relationships and performance. Clinicians often rate the degree of leukoaraiosis by subjective experience or simple stereological grid measurements, while deep medullary veins in the brain are still small in diameter, blurred and difficult to distinguish in the most obvious ESWAN images. These subjective evaluations can not provide an accurate argument, so a reliable computer-aided method is needed to help doctors accurately extract the diseased areas of leukoaraiosis and the deep medullary veins in the brain. In view of this demand, according to the imaging characteristics of two kinds of pathological regions in multi-mode images, The computer-aided diagnosis method for automatic extraction of the main trunk of deep medullary vein in T2FLAIR image and ESWAN image was studied. It was divided into three major steps: first, the skull stripping preprocessing of T2FLAIR image was carried out; Then the loose area of white matter was extracted from T2FLAIR image and the main trunk of deep medullary vein was extracted from the corresponding region of ESWAN image (corresponding to the loose area of white matter in T2FLAIR image). The preprocessing of skull dissection, using Otsu threshold method and morphology method, can effectively peel off non-brain tissue which interferes with white matter segmentation. In the experiment, this method is more accurate than the traditional BSE skull stripping method in Jaccard similarity measure and Dice similarity measure, compared with manual fine segmentation gold standard. Firstly, the initial contour of the brain white matter loose area is determined by the method of Otsu threshold, and then an improved C-V model is used to iterate the evolution of the initial contour. The experimental results show that the proposed method performs better than the level set method without initialization proposed by Li in the segmentation effect and the segmentation time is better. Finally, using the relationship between the eigenvalue of Hessian matrix and the direction of gray change of deep medullary vein in brain, a filter was designed to extract the main trunk of deep medullary vein in brain. Combined with morphological post-processing to disconnect the trunk and eliminate the pseudo-trunk, the result is close to the gold standard of manual fine segmentation.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：R742.89

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